We also wanted to investigate what happens when we run a similar analysis, but we split groups based on DFR accuracy.
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(ggplot2)
library(reshape2)
library(psych)
##
## Attaching package: 'psych'
## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha
library(patchwork)
library(rockchalk)
##
## Attaching package: 'rockchalk'
## The following object is masked from 'package:dplyr':
##
## summarize
load('data/load_effects_DFR.RData')
load('data/behav.RData')
load('data/structural_measures.RData')
load('data/connectivity_data.RData')
source("split_into_groups.R")
source("prep_split_for_bar_plots.R")
source("plot_bars.R")
acc <- p200_data[p200_data$PTID %in% p200_indiv_ROI_DFR_delay$PTID,]
acc <- merge(acc,construct_vars_omnibus)
data_for_plot <- merge(p200_indiv_ROI_DFR_delay,acc)
p1 <- ggplot(data_for_plot, aes(x=XDFR_MRI_ACC_L3, y = DFR_ROIs))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.85,y=2.25,label="r = 0.25**")
p2 <- ggplot(data_for_plot, aes(x=XDFR_MRI_ACC_L3, y = DFR_L_dlPFC))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=1,label="r = 0.26***")
p3 <- ggplot(data_for_plot, aes(x=XDFR_MRI_ACC_L3, y = DFR_L_IPS))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.85,y=.9,label="r = 0.24**")
p4 <- ggplot(data_for_plot, aes(x=XDFR_MRI_ACC_L3, y = DFR_L_preSMA))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=1,label="r = 0.28***")
(p1+p2)/(p3+p4)
#sort by omnibus span
p200_data_sorted <- acc[order(acc$XDFR_MRI_ACC_L3),]
low_DFR <- p200_data_sorted[1:56,]
med_DFR <- p200_data_sorted[58:113,]
high_DFR <-p200_data_sorted[115:170,]
low_DFR$level <- "low"
med_DFR$level <- "med"
high_DFR$level <- "high"
things_to_hist <- rbind(low_DFR,med_DFR,high_DFR)
DFR_groups <- list(high=data.frame(high_DFR),med=data.frame(med_DFR), low=data.frame(low_DFR), all=data.frame(things_to_hist))
save(list=c("DFR_groups", "p200_data_sorted"),file="data/DFR_split_groups_info.RData")
low_med_split_DFR <- p200_data_sorted[1:85,]
high_med_split_DFR <- p200_data_sorted[86:170,]
low_med_split_DFR$level <- "low_median"
high_med_split_DFR$level <- "high_median"
load("~/Documents/Code/RDoC_for_GitHub/data/split_WM_groups_fMRI.RData")
DFR_med_split <- rbind(low_med_split_DFR,high_med_split_DFR)
DFR_split_data <- dplyr::select(DFR_groups[["all"]],"PTID","XDFR_MRI_ACC_L3","level")
WM_split_data <- dplyr::select(split_constructs[["all"]],"PTID","omnibus_span_no_DFR_MRI","level")
DFR_med_split_data <- dplyr::select(DFR_med_split,"PTID","level")
colnames(DFR_split_data)[3] <- "DFR_level"
colnames(WM_split_data)[3] <- "WM_level"
colnames(DFR_med_split_data)[2] <- "DFR_med_level"
comparison <- merge(DFR_split_data,WM_split_data,by="PTID")
comparison <- merge(comparison, DFR_med_split_data,by="PTID")
comparison <- dplyr::select(comparison,"PTID", "omnibus_span_no_DFR_MRI","XDFR_MRI_ACC_L3", "WM_level","DFR_level","DFR_med_level")
comparison_counts <- data.frame(matrix(nrow=3,ncol=3))
colnames(comparison_counts) <- c("low_WM","med_WM","high_WM")
rownames(comparison_counts) <- c("low_DFR","med_DFR","high_DFR")
for (row in seq.int(1,3)){
for (col in seq.int(1,3)){
comparison_counts[row,col] <- sum(substr(comparison$WM_level,1,3)==substr(colnames(comparison_counts)[row],1,3) & substr(comparison$DFR_level,1,3)==substr(colnames(comparison_counts)[col],1,3))
}
}
comparison_counts_melt <- data.frame(matrix(ncol=3,nrow=9))
colnames(comparison_counts_melt) <- c("WM","DFR","count")
row_count <- 1
for (row in seq.int(1,3)){
for (col in seq.int(1,3)){
comparison_counts_melt$WM[row_count] <- colnames(comparison_counts)[row]
comparison_counts_melt$DFR[row_count] <- rownames(comparison_counts)[col]
comparison_counts_melt$count[row_count] <- comparison_counts[row,col]
row_count = row_count+1
}
}
comparison_counts_melt$WM <- factor(comparison_counts_melt$WM,levels = c("low_WM","med_WM","high_WM"))
comparison_counts_melt$DFR <- factor(comparison_counts_melt$DFR,levels=c("low_DFR","med_DFR","high_DFR"))
Subjects split on DFR high load accuracy. 56 subjects per group. Approximately 50% of subjects stay in their initial group.
ggplot(data=comparison_counts_melt)+
geom_tile(aes(x=WM,y=DFR, fill=count))+
geom_text(aes(x=WM,y=DFR,label=count),size=8,color="white")+
theme(legend.position = "none")
comparison_counts_median <- data.frame(matrix(nrow=2,ncol=3))
colnames(comparison_counts_median) <- c("low_WM","med_WM","high_WM")
rownames(comparison_counts_median) <- c("low_DFR_median","high_DFR_median")
comparison_counts_median[1,1] <- sum(comparison$WM_level == "low" & comparison$DFR_med_level == "low_median")
comparison_counts_median[2,1] <- sum(comparison$WM_level == "low" & comparison$DFR_med_level == "high_median")
comparison_counts_median[1,2] <- sum(comparison$WM_level == "med" & comparison$DFR_med_level == "low_median")
comparison_counts_median[2,2] <- sum(comparison$WM_level == "med" & comparison$DFR_med_level == "high_median")
comparison_counts_median[1,3] <- sum(comparison$WM_level == "high" & comparison$DFR_med_level == "low_median")
comparison_counts_median[2,3] <- sum(comparison$WM_level == "high" & comparison$DFR_med_level == "high_median")
comparison_counts_median_melt <- data.frame(matrix(ncol=3,nrow=6))
colnames(comparison_counts_median_melt) <- c("WM","DFR_median","count")
row_count <- 1
for (row in seq.int(1,2)){
for (col in seq.int(1,3)){
comparison_counts_median_melt$WM[row_count] <- colnames(comparison_counts_median)[col]
comparison_counts_median_melt$DFR_median[row_count] <- rownames(comparison_counts_median)[row]
comparison_counts_median_melt$count[row_count] <- comparison_counts_median[row,col]
row_count = row_count+1
}
}
comparison_counts_median_melt$WM <- factor(comparison_counts_median_melt$WM,levels = c("low_WM","med_WM","high_WM"))
comparison_counts_median_melt$DFR <- factor(comparison_counts_median_melt$DFR_median,levels=c("low_DFR_median","high_DFR_median"))
Later, we’re going to make our lives easier by just looking at DFR split on median (high scorers and low scorers), so let’s look at this as well.
We tend to see that if you have at least medium capacity, you tend to do well on the task.
ggplot(data=comparison_counts_median_melt)+
geom_tile(aes(x=WM,y=DFR_median, fill=count))+
geom_text(aes(x=WM,y=DFR_median,label=count),size=8,color="white")
split_constructs <- split_into_groups(constructs_fMRI[1:7],DFR_groups)
split_clinical <- split_into_groups(p200_clinical_zscores, DFR_groups)
split_DFR_delay <- split_into_groups(p200_indiv_ROI_DFR_delay, DFR_groups)
split_DFR_cue <- split_into_groups(p200_indiv_ROI_DFR_cue, DFR_groups)
split_DFR_probe <- split_into_groups(p200_indiv_ROI_DFR_probe, DFR_groups)
split_DFR_FFA <- split_into_groups(p200_FFA,DFR_groups)
split_DFR_HPC_Ant <- split_into_groups(p200_HPC_Ant, DFR_groups)
split_DFR_HPC_Med <- split_into_groups(p200_HPC_Med, DFR_groups)
split_DFR_HPC_Post <- split_into_groups(p200_HPC_Post, DFR_groups)
split_fullMask_delay <- split_into_groups(p200_DFR_full_mask, DFR_groups)
split_cue_ROIs <- split_into_groups(p200_indiv_ROI_delayDFR_cuePeriod, DFR_groups)
split_demographics <- split_into_groups(p200_demographics,DFR_groups)
split_cortical_thickness_DFR <- split_into_groups(p200_DFR_fullMask_cortical_thickness,DFR_groups)
split_RS <- split_into_groups(p200_all_RS,DFR_groups)
split_beta_conn_cue <- split_into_groups(p200_beta_conn_cue,DFR_groups)
split_beta_conn_delay <- split_into_groups(p200_beta_conn_delay,DFR_groups)
split_BCT <- split_into_groups(p200_BCT_forCorr,DFR_groups)
split_indiv_partic_coeff <- split_into_groups(p200_indiv_network_ParticCoeff,DFR_groups)
save(list=c("split_constructs","split_clinical","split_DFR_delay", "split_DFR_cue", "split_DFR_probe", "split_DFR_FFA", "split_DFR_HPC_Ant", "split_DFR_HPC_Med", "split_DFR_HPC_Post", "split_fullMask_delay", "split_cue_ROIs", "split_demographics","split_cortical_thickness_DFR","split_RS","split_beta_conn_cue","split_beta_conn_delay","split_BCT", "split_indiv_partic_coeff"), file="data/split_DFR_groups_fMRI.RData")
split_means_demo <- data.frame(matrix(nrow=length(split_demographics)-1,ncol=8))
colnames(split_means_demo) <- c("Trio","Prisma","CS","NCS","female","male","age","age_se")
rownames(split_means_demo) <- names(split_demographics)[1:length(names(split_demographics))-1]
for (level in seq.int(1,length(split_demographics)-1)){
split_means_demo$Trio[level] <- length(split_demographics[[level]]$SCANNER[split_demographics[[level]]$SCANNER==1])
split_means_demo$Prisma[level] <- length(split_demographics[[level]]$SCANNER[split_demographics[[level]]$SCANNER==2])
split_means_demo$CS[level] <- length(split_demographics[[level]]$GROUP[split_demographics[[level]]$GROUP==1])
split_means_demo$NCS[level] <- length(split_demographics[[level]]$GROUP[split_demographics[[level]]$GROUP==2])
split_means_demo$female[level] <- length(split_demographics[[level]]$GENDER[split_demographics[[level]]$GENDER==2])
split_means_demo$male[level] <- length(split_demographics[[level]]$GENDER[split_demographics[[level]]$GENDER==1])
split_means_demo$age[level] <- mean(split_demographics[[level]]$AGE,na.rm=TRUE)
split_means_demo$age_se[level] <- sd(split_demographics[[level]]$AGE,na.rm=TRUE)/sqrt(length(split_demographics[[level]]$AGE[!is.na(split_demographics[[level]]$AGE)]))
}
split_means_demo$level <- as.factor(c("high", "med","low"))
means_melt_demo <- melt(split_means_demo,id.vars="level")
Most notable thing here is that there is a larger proportion of CS than NCS in the low performing group. Also seeing that there are more females in the mid performing group.
age_plot <- ggplot(data=split_means_demo,aes(x=level,y=age))+
geom_bar(stat="identity",width = .5, color = "#667Ea4", fill = "#667Ea4")+
geom_errorbar(aes(ymin=age-age_se,ymax=age+age_se),width=.2)+
ggtitle("Age") +
ylab("Mean +/- SE") +
scale_x_discrete(limits = c("low","med","high")) +
theme(aspect.ratio = 1)
scanner_data <- demo_plot_data.m[demo_plot_data.m$variable=="scanner_count",c(1,2,5,6)]
scanner_data$value <- scanner_data$value/56*100
scanner_plot <- ggplot(scanner_data,aes(x=level,y=value,fill=scanner))+
geom_bar(stat="identity") +
ylab("Percent (%)") +
theme(aspect.ratio=1) +
scale_x_discrete(limits = c("low","med","high")) +
ggtitle("Scanner")
gender_data <- demo_plot_data.m[demo_plot_data.m$variable=="gender_count",c(1,3,5,6)]
gender_data$value <- gender_data$value/56*100
gender_plot <- ggplot(gender_data,aes(x=level,y=value, fill=gender))+
geom_bar(stat="identity") +
ylab("Percent (%)") +
theme(aspect.ratio=1) +
scale_x_discrete(limits = c("low","med","high")) +
ggtitle("Gender")
care_data <- demo_plot_data.m[demo_plot_data.m$variable=="care_count",c(1,4:6)]
care_data$value <- care_data$value/56*100
care_plot <- ggplot(care_data,aes(x=level,y=value, fill=care))+
geom_bar(stat="identity") +
ylab("Percent (%)") +
theme(aspect.ratio=1) +
scale_x_discrete(limits = c("low","med","high")) +
ggtitle("CS vs NCS")
(age_plot + gender_plot)/(care_plot + scanner_plot)+
plot_annotation(title="Demographics split by DFR performance")
melt_constructs <- prep_split_for_bar_plots(DFR_groups)
melt_clinical <- prep_split_for_bar_plots(split_clinical)
melt_DFR_delay <- prep_split_for_bar_plots(split_DFR_delay)
melt_DFR_cue <- prep_split_for_bar_plots(split_DFR_cue)
melt_DFR_probe <- prep_split_for_bar_plots(split_DFR_probe)
melt_DFR_FFA <- prep_split_for_bar_plots(split_DFR_FFA)
melt_DFR_HPC_Ant <- prep_split_for_bar_plots(split_DFR_HPC_Ant)
melt_DFR_HPC_Med <- prep_split_for_bar_plots(split_DFR_HPC_Med)
melt_DFR_HPC_Post <- prep_split_for_bar_plots(split_DFR_HPC_Post)
melt_fullMask_delay <- prep_split_for_bar_plots(split_fullMask_delay)
melt_cue_ROIs <- prep_split_for_bar_plots(split_cue_ROIs)
melt_cortical_thickness_DFR <- prep_split_for_bar_plots(split_cortical_thickness_DFR)
melt_RS <- prep_split_for_bar_plots(split_RS)
melt_beta_conn_cue <- prep_split_for_bar_plots(split_beta_conn_cue)
melt_beta_conn_delay <- prep_split_for_bar_plots(split_beta_conn_delay)
melt_BCT <- prep_split_for_bar_plots(split_BCT)
melt_indiv_partic_coeff <- prep_split_for_bar_plots(split_indiv_partic_coeff)
constructs_plots <- plot_bars(melt_constructs)
clinical_plots <- plot_bars(melt_clinical)
DFR_delay_plots <- plot_bars(melt_DFR_delay)
DFR_cue_plots <- plot_bars(melt_DFR_cue)
DFR_probe_plots <- plot_bars(melt_DFR_probe)
DFR_FFA_plots <- plot_bars(melt_DFR_FFA)
DFR_HPC_Ant_plots <- plot_bars(melt_DFR_HPC_Ant)
DFR_HPC_Med_plots <- plot_bars(melt_DFR_HPC_Med)
DFR_HPC_Post_plots <- plot_bars(melt_DFR_HPC_Post)
fullMask_delay_plots <- plot_bars(melt_fullMask_delay)
cue_ROIs_plots <- plot_bars(melt_cue_ROIs)
cortical_thickness_plots <- plot_bars(melt_cortical_thickness_DFR)
RS_plots <- plot_bars(melt_RS)
beta_conn_cue_plots <- plot_bars(melt_beta_conn_cue)
beta_conn_delay_plots <- plot_bars(melt_beta_conn_delay)
BCT_plots <- plot_bars(melt_BCT)
indiv_partic_coeff_plots <- plot_bars(melt_indiv_partic_coeff)
A nice sanity check here as well - if a subject has higher capacity and higher intelligence, they tend to have higher performance. The main statistically significant differences here are in omnibus span, where high > low.
constructs_plots[["omnibus_span_no_DFR_MRI"]]$labels$title = "Omnibus Span"
(constructs_plots[["omnibus_span_no_DFR_MRI"]]+constructs_plots[["intelligence"]]+constructs_plots[["LTM"]]) +
plot_annotation(title="Constructs split on DFR performance")
print("Omnibus Span")
## [1] "Omnibus Span"
span.aov <- aov(omnibus_span_no_DFR_MRI ~ level, data=split_constructs[["all"]])
summary(span.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.84 1.4193 5.096 0.00712 **
## Residuals 165 45.95 0.2785
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(span.aov)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = omnibus_span_no_DFR_MRI ~ level, data = split_constructs[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.1305318 -0.3664026 0.10533899 0.3922824
## low-high -0.3167678 -0.5526386 -0.08089705 0.0050411
## low-med -0.1862360 -0.4221068 0.04963473 0.1514683
print("LTM")
## [1] "LTM"
LTM.aov <- aov(LTM ~ level, data=split_constructs[["all"]])
summary(LTM.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 3.12 1.5605 2.822 0.0624 .
## Residuals 161 89.02 0.5529
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 4 observations deleted due to missingness
TukeyHSD(LTM.aov)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = LTM ~ level, data = split_constructs[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.1549342 -0.4903700 0.180501649 0.5200105
## low-high -0.3381273 -0.6751126 -0.001142138 0.0490211
## low-med -0.1831932 -0.5201784 0.153792056 0.4051248
print("Intelligence")
## [1] "Intelligence"
intelligence.aov <- aov(intelligence ~ level, data=split_constructs[["all"]])
summary(intelligence.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.17 0.5867 0.999 0.37
## Residuals 165 96.87 0.5871
TukeyHSD(intelligence.aov)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = intelligence ~ level, data = split_constructs[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.02305072 -0.3655146 0.3194132 0.9861279
## low-high -0.18768753 -0.5301514 0.1547763 0.3993499
## low-med -0.16463682 -0.5071007 0.1778271 0.4927506
There is a significant negative correlation between BPRS and performance - this doesn’t come through quite as well in the split groups, but we definitely still see the pattern and there is a trend in differences in the ANOVA.
ggplot(data=data_for_plot, aes(x=XDFR_MRI_ACC_L3, y = WHO_ST_S32))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=55,label="r=-0.13")+
ggtitle("Correlation of WHODAS and DFR Performance")
print("WHODAS")
## [1] "WHODAS"
print(cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$WHO_ST_S32))
##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$WHO_ST_S32
## t = -1.6545, df = 168, p-value = 0.0999
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.27194790 0.02436222
## sample estimates:
## cor
## -0.1266163
ggplot(data=data_for_plot, aes(x=XDFR_MRI_ACC_L3, y = BPRS_TOT))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=.9,y=70,label="r=-0.19*")+
ggtitle("Correlation of BPRS and DFR Performance")
print("BPRS")
## [1] "BPRS"
print(cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$BPRS_TOT))
##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$BPRS_TOT
## t = -2.5715, df = 168, p-value = 0.01099
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.33529800 -0.04542107
## sample estimates:
## cor
## -0.1946049
clinical_plots[["WHO_ST_S32"]]$labels$title <- "WHODAS"
clinical_plots[["BPRS"]]$labels$title <- "BPRS"
(clinical_plots[["WHO_ST_S32"]] + clinical_plots[["BPRS_TOT"]])+
plot_annotation(title="Clinical measures split by DFR performance")
print("WHODAS")
## [1] "WHODAS"
WHODAS.aov <- aov(WHO_ST_S32 ~ level, data=split_clinical[["all"]])
summary(WHODAS.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 3.61 1.8040 1.857 0.159
## Residuals 165 160.32 0.9717
print("BPRS")
## [1] "BPRS"
BPRS.aov <- aov(BPRS_TOT ~ level, data=split_clinical[["all"]])
summary(BPRS.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 5.45 2.7239 2.844 0.0611 .
## Residuals 165 158.04 0.9578
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
There is a linear relationship between performance and load effect in the cue mask during the delay period.
fullMask_delay_plots[["cue_low"]]+fullMask_delay_plots[["cue_high"]]+fullMask_delay_plots[["cue_loadEffect"]]+
plot_annotation(title="BOLD signal from full delay period mask during cue period")
print("Load Effect")
## [1] "Load Effect"
cue_LE.aov <- aov(cue_loadEffect ~ level, data=split_fullMask_delay[["all"]])
summary(cue_LE.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.944 0.4722 3.082 0.0485 *
## Residuals 165 25.284 0.1532
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(cue_LE.aov)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = cue_loadEffect ~ level, data = split_fullMask_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.10768817 -0.2826502 0.067273864 0.3150380
## low-high -0.18268512 -0.3576472 -0.007723088 0.0384648
## low-med -0.07499695 -0.2499590 0.099965080 0.5692292
Let’s look at some scatter plots for the more linear looking things - particularly the high load and the load effect.
data_for_plot <- merge(p200_data,p200_DFR_full_mask,by="PTID")
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=cue_high))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=2,label="r=0.25**")+
ggtitle("High Load")
print("High Load")
## [1] "High Load"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$cue_high)
##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$cue_high
## t = 3.32, df = 168, p-value = 0.001104
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1014029 0.3842921
## sample estimates:
## cor
## 0.2481301
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=cue_loadEffect))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=1.5,label="r=0.24**")+
ggtitle("Load Effect")
print("Load Effect")
## [1] "Load Effect"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$cue_loadEffect)
##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$cue_loadEffect
## t = 3.2502, df = 168, p-value = 0.001393
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.09623747 0.37983718
## sample estimates:
## cor
## 0.2432285
The only significant thing here is the occipital regions - more decrease in
DFR_cue_plots[["L_FEF_low"]] + DFR_cue_plots[["L_FEF_high"]] + DFR_cue_plots[["L_FEF_loadEffect"]]
DFR_cue_plots[["L_insula_low"]] + DFR_cue_plots[["L_insula_high"]] + DFR_cue_plots[["L_insula_loadEffect"]]
DFR_cue_plots[["L_IPS_low"]] + DFR_cue_plots[["L_IPS_high"]] + DFR_cue_plots[["L_IPS_loadEffect"]]
DFR_cue_plots[["L_occipital_low"]] + DFR_cue_plots[["L_occipital_high"]] + DFR_cue_plots[["L_occipital_loadEffect"]]
DFR_cue_plots[["R_FEF_low"]] + DFR_cue_plots[["R_FEF_high"]] + DFR_cue_plots[["R_FEF_loadEffect"]]
DFR_cue_plots[["R_insula_low"]] + DFR_cue_plots[["R_insula_high"]] + DFR_cue_plots[["R_insula_loadEffect"]]
DFR_cue_plots[["R_IPS_low"]] + DFR_cue_plots[["R_IPS_high"]] + DFR_cue_plots[["R_IPS_loadEffect"]]
DFR_cue_plots[["R_MFG_low"]] + DFR_cue_plots[["R_MFG_high"]] + DFR_cue_plots[["R_MFG_loadEffect"]]
DFR_cue_plots[["R_preSMA_low"]] + DFR_cue_plots[["R_preSMA_high"]] + DFR_cue_plots[["R_preSMA_loadEffect"]]
DFR_cue_plots[["R_occipital_low"]] + DFR_cue_plots[["R_occipital_high"]] + DFR_cue_plots[["R_occipital_loadEffect"]]
print("L FEF")
## [1] "L FEF"
cue_L_FEF.aov <- aov(L_FEF_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_L_FEF.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.55 0.2764 1.227 0.296
## Residuals 165 37.17 0.2253
print("L insula")
## [1] "L insula"
cue_L_insula.aov <- aov(L_insula_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_L_insula.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.88 0.4398 1.676 0.19
## Residuals 165 43.29 0.2624
print("L IPS")
## [1] "L IPS"
cue_L_IPS.aov <- aov(L_IPS_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_L_IPS.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.91 0.9541 2.295 0.104
## Residuals 165 68.60 0.4157
print("L occipital")
## [1] "L occipital"
cue_L_occipital.aov <- aov(L_occipital_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_L_occipital.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 3.30 1.648 3.59 0.0298 *
## Residuals 165 75.74 0.459
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print("R FEF")
## [1] "R FEF"
cue_R_FEF.aov <- aov(R_FEF_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_FEF.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.08 1.0387 1.907 0.152
## Residuals 165 89.87 0.5447
print("R insula")
## [1] "R insula"
cue_R_insula.aov <- aov(R_insula_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_insula.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.44 0.2221 1.007 0.367
## Residuals 165 36.39 0.2205
print("R IPS")
## [1] "R IPS"
cue_R_IPS.aov <- aov(R_IPS_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_IPS.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.85 0.9259 2.374 0.0962 .
## Residuals 165 64.34 0.3899
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print("R MFG")
## [1] "R MFG"
cue_R_MFG.aov <- aov(R_MFG_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_MFG.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.597 0.2987 1.594 0.206
## Residuals 165 30.910 0.1873
print("R occipital")
## [1] "R occipital"
cue_R_occipital.aov <- aov(R_occipital_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_occipital.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 3.81 1.9029 4.48 0.0127 *
## Residuals 165 70.09 0.4248
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print("R preSMA")
## [1] "R preSMA"
cue_R_preSMA.aov <- aov(R_preSMA_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_preSMA.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.71 0.3540 0.759 0.47
## Residuals 165 76.97 0.4665
There are significant differnces in the high load and load effect - the high load trials have differences: high > low and high > medium, while the load effect only has high > low.
fullMask_delay_plots[["delay_low"]]+fullMask_delay_plots[["delay_high"]]+fullMask_delay_plots[["delay_loadEffect"]]+
plot_annotation(title="BOLD signal from full delay period mask during delay period")
print("Low Load")
## [1] "Low Load"
delay_L1.aov <- aov(delay_low ~ level, data=split_fullMask_delay[["all"]])
summary(delay_L1.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.051 0.02533 0.833 0.437
## Residuals 165 5.018 0.03042
TukeyHSD(delay_L1.aov)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = delay_low ~ level, data = split_fullMask_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.01471531 -0.09266343 0.06323281 0.8960257
## low-high 0.02720635 -0.05074177 0.10515447 0.6877751
## low-med 0.04192166 -0.03602646 0.11986978 0.4130273
print("High Load")
## [1] "High Load"
delay_L3.aov <- aov(delay_high ~ level, data=split_fullMask_delay[["all"]])
summary(delay_L3.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.501 0.25048 7.329 0.000892 ***
## Residuals 165 5.639 0.03417
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(delay_L3.aov)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = delay_high ~ level, data = split_fullMask_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.118340890 -0.20096672 -0.03571506 0.0025291
## low-high -0.113164799 -0.19579063 -0.03053897 0.0041193
## low-med 0.005176091 -0.07744974 0.08780192 0.9879721
print("Load Effect")
## [1] "Load Effect"
delay_LE.aov <- aov(delay_loadEffect ~ level, data=split_fullMask_delay[["all"]])
summary(delay_LE.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.593 0.29673 6.214 0.0025 **
## Residuals 165 7.880 0.04776
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(delay_LE.aov)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = delay_loadEffect ~ level, data = split_fullMask_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.10362558 -0.2012982 -0.005952923 0.0347174
## low-high -0.14037115 -0.2380438 -0.042698489 0.0024325
## low-med -0.03674557 -0.1344182 0.060927091 0.6475245
Let’s look at some scatter plots for the more linear looking things - particularly the high load and the load effect.
data_for_plot <- merge(p200_data,p200_DFR_full_mask,by="PTID")
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=delay_high))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=0.6,label="r=0.26***")+
ggtitle("High Load")
print("High Load")
## [1] "High Load"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$delay_high)
##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$delay_high
## t = 3.5615, df = 168, p-value = 0.00048
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1191920 0.3995341
## sample estimates:
## cor
## 0.264953
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=delay_loadEffect))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=1,label="r=0.26***")+
ggtitle("Load Effect")
print("Load Effect")
## [1] "Load Effect"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$delay_loadEffect)
##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$delay_loadEffect
## t = 3.4403, df = 168, p-value = 0.0007331
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1102834 0.3919204
## sample estimates:
## cor
## 0.2565393
No L dMFG, all show high > low, except for . L aMFG, L dlPFC, R dlPFC also showed high > med, and R medial parietal only showed high > med.
(DFR_delay_plots[["DFR_L_aMFG"]] + DFR_delay_plots[["DFR_L_dlPFC"]] + DFR_delay_plots[["DFR_L_dMFG"]]) + plot_annotation(title="individual DFR delay period ROIs")
(DFR_delay_plots[["DFR_L_IPS"]] + DFR_delay_plots[["DFR_L_preSMA"]] + DFR_delay_plots[["DFR_R_dlPFC"]])
(DFR_delay_plots[["DFR_R_dMFG"]] + DFR_delay_plots[["DFR_R_IPS"]] + DFR_delay_plots[["DFR_R_medParietal"]])
print("L aMFG")
## [1] "L aMFG"
L_aMFG.aov <- aov(DFR_L_aMFG ~ level, data=split_DFR_delay[["all"]])
summary(L_aMFG.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.431 0.7155 9.074 0.000182 ***
## Residuals 165 13.011 0.0789
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(L_aMFG.aov)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DFR_L_aMFG ~ level, data = split_DFR_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.14467585 -0.2701834 -0.01916829 0.0193402
## low-high -0.22277889 -0.3482864 -0.09727133 0.0001295
## low-med -0.07810304 -0.2036106 0.04740451 0.3071420
print("L dlPFC")
## [1] "L dlPFC"
L_dlPFC.aov <- aov(DFR_L_dlPFC ~ level, data=split_DFR_delay[["all"]])
summary(L_dlPFC.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.976 0.4878 7.024 0.00118 **
## Residuals 165 11.459 0.0694
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(L_dlPFC.aov)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DFR_L_dlPFC ~ level, data = split_DFR_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.12027344 -0.2380607 -0.002486185 0.0441621
## low-high -0.18376405 -0.3015513 -0.065976794 0.0008848
## low-med -0.06349061 -0.1812779 0.054296649 0.4113955
print("L dMFG")
## [1] "L dMFG"
L_dMFG.aov <- aov(DFR_L_dMFG ~ level, data=split_DFR_delay[["all"]])
summary(L_dMFG.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.315 0.15733 2.883 0.0588 .
## Residuals 165 9.005 0.05458
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print("L IPS")
## [1] "L IPS"
L_IPS.aov <- aov(DFR_L_IPS ~ level, data=split_DFR_delay[["all"]])
summary(L_IPS.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.572 0.28619 5.685 0.0041 **
## Residuals 165 8.306 0.05034
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(L_IPS.aov)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DFR_L_IPS ~ level, data = split_DFR_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.06108331 -0.1613662 0.03919954 0.3225542
## low-high -0.14249284 -0.2427757 -0.04220999 0.0027663
## low-med -0.08140953 -0.1816924 0.01887332 0.1362678
print("L preSMA")
## [1] "L preSMA"
L_preSMA.aov <- aov(DFR_L_preSMA ~ level, data=split_DFR_delay[["all"]])
summary(L_preSMA.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.867 0.4334 7.62 0.000684 ***
## Residuals 165 9.385 0.0569
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(L_preSMA.aov)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DFR_L_preSMA ~ level, data = split_DFR_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.04077966 -0.1473775 0.06581818 0.6380645
## low-high -0.16862032 -0.2752182 -0.06202248 0.0007351
## low-med -0.12784066 -0.2344385 -0.02124282 0.0141448
print("R dlPFC")
## [1] "R dlPFC"
R_dlPFC.aov <- aov(DFR_R_dlPFC ~ level, data=split_DFR_delay[["all"]])
summary(R_dlPFC.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.853 0.4264 5.675 0.00414 **
## Residuals 165 12.398 0.0751
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(R_dlPFC.aov)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DFR_R_dlPFC ~ level, data = split_DFR_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.15604359 -0.2785624 -0.03352479 0.0083964
## low-high -0.14570503 -0.2682238 -0.02318623 0.0151450
## low-med 0.01033856 -0.1121802 0.13285736 0.9782852
print("R dMFG")
## [1] "R dMFG"
R_dMFG.aov <- aov(DFR_R_dMFG ~ level, data=split_DFR_delay[["all"]])
summary(R_dMFG.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.70 0.350 5.223 0.00632 **
## Residuals 165 11.06 0.067
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(R_dMFG.aov)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DFR_R_dMFG ~ level, data = split_DFR_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.12285140 -0.2385489 -0.007153935 0.0345290
## low-high -0.14763033 -0.2633278 -0.031932865 0.0082542
## low-med -0.02477893 -0.1404764 0.090918540 0.8682684
print("R IPS")
## [1] "R IPS"
R_IPS.aov <- aov(DFR_R_IPS ~ level, data=split_DFR_delay[["all"]])
summary(R_IPS.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.714 0.3569 4.893 0.00863 **
## Residuals 165 12.035 0.0729
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(R_IPS.aov)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DFR_R_IPS ~ level, data = split_DFR_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.09424707 -0.2149556 0.02646142 0.1578139
## low-high -0.15872652 -0.2794350 -0.03801803 0.0062119
## low-med -0.06447945 -0.1851879 0.05622904 0.4179414
print("R medial Parietal")
## [1] "R medial Parietal"
R_medParietal.aov <- aov(DFR_R_medParietal ~ level, data=split_DFR_delay[["all"]])
summary(R_medParietal.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.56 0.7820 3.956 0.021 *
## Residuals 165 32.61 0.1977
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(R_medParietal.aov)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DFR_R_medParietal ~ level, data = split_DFR_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.22691284 -0.4256253 -0.02820033 0.0207656
## low-high -0.17071269 -0.3694252 0.02799981 0.1078362
## low-med 0.05620015 -0.1425124 0.25491265 0.7818678
Let’s look at some scatter plots for the more linear looking things - particularly the high load and the load effect.
data_for_plot <- merge(p200_data,p200_indiv_ROI_DFR_delay,by="PTID")
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=DFR_L_aMFG))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=1,label="r=0.27***")+
ggtitle("L aMFG")
print("L aMFG")
## [1] "L aMFG"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$DFR_L_aMFG)
##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$DFR_L_aMFG
## t = 3.6757, df = 168, p-value = 0.0003188
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1275544 0.4066460
## sample estimates:
## cor
## 0.2728306
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=DFR_L_dlPFC))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=1,label="r=0.26***")+
ggtitle("L dlPFC")
print("L dlPFC")
## [1] "L dlPFC"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$DFR_L_dlPFC)
##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$DFR_L_dlPFC
## t = 3.5387, df = 168, p-value = 0.0005202
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1175198 0.3981079
## sample estimates:
## cor
## 0.2633753
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=DFR_L_dMFG))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=0.75,label="r=0.16*")+
ggtitle("L dMFG")
print("L dMFG")
## [1] "L dMFG"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$DFR_L_dMFG)
##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$DFR_L_dMFG
## t = 2.0918, df = 168, p-value = 0.03796
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.009023415 0.302579639
## sample estimates:
## cor
## 0.1593213
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=DFR_L_IPS))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=0.75,label="r=0.24**")+
ggtitle("L IPS")
print("L IPS")
## [1] "L IPS"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$DFR_L_IPS)
##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$DFR_L_IPS
## t = 3.2651, df = 168, p-value = 0.001326
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.09734466 0.38079319
## sample estimates:
## cor
## 0.2442798
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=DFR_L_preSMA))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=1,label="r=0.28***")+
ggtitle("L preSMA")
print("L preSMA")
## [1] "L preSMA"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$DFR_L_preSMA)
##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$DFR_L_preSMA
## t = 3.7087, df = 168, p-value = 0.0002828
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1299569 0.4086830
## sample estimates:
## cor
## 0.2750902
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=DFR_R_dlPFC))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=1.25,label="r=0.23**")+
ggtitle("R dlPFC")
print("R dlPFC")
## [1] "R dlPFC"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$DFR_R_dlPFC)
##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$DFR_R_dlPFC
## t = 3.1068, df = 168, p-value = 0.002221
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.08557902 0.37060306
## sample estimates:
## cor
## 0.2330909
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=DFR_R_dMFG))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=1.25,label="r=0.20**")+
ggtitle("R dMFG")
print("R dMFG")
## [1] "R dMFG"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$DFR_R_dMFG)
##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$DFR_R_dMFG
## t = 2.7004, df = 168, p-value = 0.007634
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.05514113 0.34391879
## sample estimates:
## cor
## 0.2039626
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=DFR_R_IPS))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=1.25,label="r=0.25**")+
ggtitle("R IPS")
print("R IPS")
## [1] "R IPS"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$DFR_R_IPS)
##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$DFR_R_IPS
## t = 3.3318, df = 168, p-value = 0.001061
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1022765 0.3850442
## sample estimates:
## cor
## 0.2489582
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=DFR_R_medParietal))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=1.75,label="r=0.18*")+
ggtitle("R medial Parietal")
print("R medial Parietal")
## [1] "R medial Parietal"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$DFR_R_medParietal)
##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$DFR_R_medParietal
## t = 2.4465, df = 168, p-value = 0.01546
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.03596677 0.32686610
## sample estimates:
## cor
## 0.1854769
No differences in the probe period.
fullMask_delay_plots[["probe_low"]]+fullMask_delay_plots[["probe_high"]]+fullMask_delay_plots[["probe_loadEffect"]]+
plot_annotation(title="BOLD signal from full delay period mask during probe period")
print("Low Load")
## [1] "Low Load"
probe_L1.aov <- aov(probe_low ~ level, data=split_fullMask_delay[["all"]])
summary(probe_L1.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.64 0.8220 1.793 0.17
## Residuals 165 75.64 0.4584
print("High Load")
## [1] "High Load"
probe_L3.aov <- aov(probe_high ~ level, data=split_fullMask_delay[["all"]])
summary(probe_L3.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.79 1.3959 2.013 0.137
## Residuals 165 114.40 0.6933
print("Load Effect")
## [1] "Load Effect"
probe_LE.aov <- aov(probe_loadEffect ~ level, data=split_fullMask_delay[["all"]])
summary(probe_LE.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.67 1.3365 2.09 0.127
## Residuals 165 105.51 0.6395
data_for_plot <- merge(p200_data,p200_DFR_full_mask,by="PTID")
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=probe_loadEffect))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=2.5,label="r=0.19*")+
ggtitle("Load Effect")
print("Load Effect")
## [1] "Load Effect"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$probe_loadEffect)
##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$probe_loadEffect
## t = 2.5975, df = 168, p-value = 0.01022
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.0473804 0.3370397
## sample estimates:
## cor
## 0.1964934
DFR_probe_plots[["dmPFC_loadEffect"]] + DFR_probe_plots[["L_aMFG_loadEffect"]] + DFR_probe_plots[["L_dlPFC_loadEffect"]] +
plot_annotation(title="individual DFR activity from probe period regions")
DFR_probe_plots[["L_insula_loadEffect"]] + DFR_probe_plots[["L_IPS_loadEffect"]] + DFR_probe_plots[["R_dlPFC_loadEffect"]]
DFR_probe_plots[["R_insula_loadEffect"]] + DFR_probe_plots[["R_OFC_loadEffect"]]
print("dmPFC")
## [1] "dmPFC"
probe_dmPFC.aov <- aov(dmPFC_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_dmPFC.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.12 0.5588 1.894 0.154
## Residuals 165 48.68 0.2950
print("L aMFG")
## [1] "L aMFG"
probe_L_aMFG.aov <- aov(L_aMFG_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_L_aMFG.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.01 0.0032 0.005 0.995
## Residuals 165 115.38 0.6992
print("L dlPFC")
## [1] "L dlPFC"
probe_L_dlPFC.aov <- aov(L_dlPFC_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_L_dlPFC.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.97 0.9826 2.253 0.108
## Residuals 165 71.98 0.4362
print("L insula")
## [1] "L insula"
probe_L_insula.aov <- aov(L_insula_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_L_insula.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.684 0.3421 1.891 0.154
## Residuals 165 29.844 0.1809
print("R dlPFC")
## [1] "R dlPFC"
probe_R_dlPFC.aov <- aov(R_dlPFC_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_R_dlPFC.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.17 0.0832 0.205 0.815
## Residuals 165 67.11 0.4068
print("R Insula")
## [1] "R Insula"
probe_R_insula.aov <- aov(R_insula_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_R_insula.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.339 0.1693 0.972 0.381
## Residuals 165 28.748 0.1742
print("R OFC")
## [1] "R OFC"
probe_R_OFC.aov <- aov(R_OFC_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_R_OFC.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.24 0.1182 0.612 0.544
## Residuals 165 31.88 0.1932
Only see difference in L FFA load effect during cue, with high > low.
DFR_FFA_plots[["L_CUE_LE"]] + DFR_FFA_plots[["L_DELAY_LE"]] + DFR_FFA_plots[["L_PROBE_LE"]]+
plot_annotation(title="FFA during DFR task")
DFR_FFA_plots[["R_CUE_LE"]] + DFR_FFA_plots[["R_DELAY_LE"]] + DFR_FFA_plots[["R_PROBE_LE"]]
print("L Cue")
## [1] "L Cue"
L_CUE_LE_FFA.aov <- aov(L_CUE_LE ~ level, data=split_DFR_FFA[["all"]])
summary(L_CUE_LE_FFA.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.69 1.3460 4.023 0.0197 *
## Residuals 164 54.87 0.3346
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(L_CUE_LE_FFA.aov)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = L_CUE_LE ~ level, data = split_DFR_FFA[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.1127546 -0.3724683 0.14695908 0.5610187
## low-high -0.3065647 -0.5651059 -0.04802350 0.0155111
## low-med -0.1938101 -0.4535238 0.06590365 0.1846592
print("R Cue")
## [1] "R Cue"
R_CUE_LE_FFA.aov <- aov(R_CUE_LE ~ level, data=split_DFR_FFA[["all"]])
summary(R_CUE_LE_FFA.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.76 0.8790 2.837 0.0615 .
## Residuals 164 50.81 0.3098
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print("L Delay")
## [1] "L Delay"
L_DELAY_LE_FFA.aov <- aov(L_DELAY_LE ~ level, data=split_DFR_FFA[["all"]])
summary(L_DELAY_LE_FFA.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.021 0.01036 0.24 0.787
## Residuals 164 7.089 0.04323
print("R Delay")
## [1] "R Delay"
R_DELAY_LE_FFA.aov <- aov(R_DELAY_LE ~ level, data=split_DFR_FFA[["all"]])
summary(R_DELAY_LE_FFA.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.069 0.03459 0.915 0.402
## Residuals 164 6.196 0.03778
print("L Probe")
## [1] "L Probe"
L_PROBE_LE_FFA.aov <- aov(L_PROBE_LE ~ level, data=split_DFR_FFA[["all"]])
summary(L_PROBE_LE_FFA.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 3.59 1.7951 1.838 0.162
## Residuals 164 160.14 0.9765
print("R Probe")
## [1] "R Probe"
R_PROBE_LE_FFA.aov <- aov(R_PROBE_LE ~ level, data=split_DFR_FFA[["all"]])
summary(R_PROBE_LE_FFA.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.89 0.4456 0.726 0.485
## Residuals 164 100.67 0.6138
data_for_plot <- merge(p200_data,p200_FFA,by="PTID")
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=R_CUE_LE))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=2.25,label="r=0.26***")+
ggtitle("R Cue LE")
print("R cue Load Effect")
## [1] "R cue Load Effect"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$R_CUE_LE)
##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$R_CUE_LE
## t = 3.5205, df = 167, p-value = 0.0005553
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1165105 0.3980145
## sample estimates:
## cor
## 0.2628475
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=L_CUE_LE))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=2.25,label="r=0.29***")+
ggtitle("L Cue LE")
print("L cue Load Effect")
## [1] "L cue Load Effect"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$L_CUE_LE)
##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$L_CUE_LE
## t = 4.0284, df = 167, p-value = 8.512e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1535378 0.4292746
## sample estimates:
## cor
## 0.2976
No significant differences in any region of the hippocampus.
DFR_HPC_Ant_plots[["L_CUE_L3"]] + DFR_HPC_Ant_plots[["L_DELAY_L3"]] + DFR_HPC_Ant_plots[["L_PROBE_L3"]]+
plot_annotation(title="HPC Ant during DFR task")
DFR_HPC_Ant_plots[["R_CUE_L3"]] + DFR_HPC_Ant_plots[["R_DELAY_L3"]] + DFR_HPC_Ant_plots[["R_PROBE_L3"]]
print("L Cue")
## [1] "L Cue"
L_CUE_L3_HPC_Ant.aov <- aov(L_CUE_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_CUE_L3_HPC_Ant.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.24 0.1198 0.521 0.595
## Residuals 164 37.69 0.2298
print("R Cue")
## [1] "R Cue"
R_CUE_L3_HPC_Ant.aov <- aov(R_CUE_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_CUE_L3_HPC_Ant.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.34 0.1718 0.778 0.461
## Residuals 164 36.21 0.2208
print("L Delay")
## [1] "L Delay"
L_DELAY_L3_HPC_Ant.aov <- aov(L_DELAY_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_DELAY_L3_HPC_Ant.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.035 0.01748 0.578 0.562
## Residuals 164 4.957 0.03023
print("R Delay")
## [1] "R Delay"
R_DELAY_L3_HPC_Ant.aov <- aov(R_DELAY_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_DELAY_L3_HPC_Ant.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.021 0.01032 0.362 0.697
## Residuals 164 4.679 0.02853
print("L Probe")
## [1] "L Probe"
L_PROBE_L3_HPC_Ant.aov <- aov(L_PROBE_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_PROBE_L3_HPC_Ant.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.31 0.1545 0.302 0.74
## Residuals 164 83.97 0.5120
print("R Probe")
## [1] "R Probe"
R_PROBE_L3_HPC_Ant.aov <- aov(R_PROBE_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_PROBE_L3_HPC_Ant.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.87 0.4354 0.763 0.468
## Residuals 164 93.54 0.5704
DFR_HPC_Ant_plots[["L_CUE_LE"]] + DFR_HPC_Ant_plots[["L_DELAY_LE"]] + DFR_HPC_Ant_plots[["L_PROBE_LE"]]+
plot_annotation(title="HPC Ant during DFR task")
DFR_HPC_Ant_plots[["R_CUE_LE"]] + DFR_HPC_Ant_plots[["R_DELAY_LE"]] + DFR_HPC_Ant_plots[["R_PROBE_LE"]]
print("L Cue")
## [1] "L Cue"
L_CUE_LE_HPC_Ant.aov <- aov(L_CUE_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_CUE_LE_HPC_Ant.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.22 0.1107 0.564 0.57
## Residuals 164 32.22 0.1965
print("R Cue")
## [1] "R Cue"
R_CUE_LE_HPC_Ant.aov <- aov(R_CUE_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_CUE_LE_HPC_Ant.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.25 0.1241 0.634 0.532
## Residuals 164 32.11 0.1958
print("L Delay")
## [1] "L Delay"
L_DELAY_LE_HPC_Ant.aov <- aov(L_DELAY_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_DELAY_LE_HPC_Ant.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.013 0.00674 0.196 0.822
## Residuals 164 5.635 0.03436
print("R Delay")
## [1] "R Delay"
R_DELAY_LE_HPC_Ant.aov <- aov(R_DELAY_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_DELAY_LE_HPC_Ant.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.029 0.01427 0.43 0.651
## Residuals 164 5.445 0.03320
print("L Probe")
## [1] "L Probe"
L_PROBE_LE_HPC_Ant.aov <- aov(L_PROBE_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_PROBE_LE_HPC_Ant.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.90 0.4505 0.756 0.471
## Residuals 164 97.78 0.5962
print("R Probe")
## [1] "R Probe"
R_PROBE_LE_HPC_Ant.aov <- aov(R_PROBE_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_PROBE_LE_HPC_Ant.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.28 0.1418 0.234 0.792
## Residuals 164 99.43 0.6063
DFR_HPC_Med_plots[["L_CUE_L3"]] + DFR_HPC_Med_plots[["L_DELAY_L3"]] + DFR_HPC_Med_plots[["L_PROBE_L3"]]+
plot_annotation(title="HPC_Med during DFR task")
DFR_HPC_Med_plots[["R_CUE_L3"]] + DFR_HPC_Med_plots[["R_DELAY_L3"]] + DFR_HPC_Med_plots[["R_PROBE_L3"]]
print("L Cue")
## [1] "L Cue"
L_CUE_L3_HPC_Med.aov <- aov(L_CUE_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_CUE_L3_HPC_Med.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.11 0.05493 0.324 0.724
## Residuals 164 27.81 0.16959
print("R Cue")
## [1] "R Cue"
R_CUE_L3_HPC_Med.aov <- aov(R_CUE_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_CUE_L3_HPC_Med.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.283 0.1417 0.846 0.431
## Residuals 164 27.480 0.1676
print("L Delay")
## [1] "L Delay"
L_DELAY_L3_HPC_Med.aov <- aov(L_DELAY_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_DELAY_L3_HPC_Med.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.019 0.009596 0.476 0.622
## Residuals 164 3.307 0.020163
print("R Delay")
## [1] "R Delay"
R_DELAY_L3_HPC_Med.aov <- aov(R_DELAY_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_DELAY_L3_HPC_Med.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.045 0.02243 1.154 0.318
## Residuals 164 3.188 0.01944
print("L Probe")
## [1] "L Probe"
L_PROBE_L3_HPC_Med.aov <- aov(L_PROBE_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_PROBE_L3_HPC_Med.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.86 0.4318 1.118 0.329
## Residuals 164 63.33 0.3862
print("R Probe")
## [1] "R Probe"
R_PROBE_L3_HPC_Med.aov <- aov(R_PROBE_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_PROBE_L3_HPC_Med.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.43 0.7165 1.871 0.157
## Residuals 164 62.80 0.3829
DFR_HPC_Med_plots[["L_CUE_LE"]] + DFR_HPC_Med_plots[["L_DELAY_LE"]] + DFR_HPC_Med_plots[["L_PROBE_LE"]]+
plot_annotation(title="HPC_Med during DFR task")
DFR_HPC_Med_plots[["R_CUE_LE"]] + DFR_HPC_Med_plots[["R_DELAY_LE"]] + DFR_HPC_Med_plots[["R_PROBE_LE"]]
print("L Cue")
## [1] "L Cue"
L_CUE_LE_HPC_Med.aov <- aov(L_CUE_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_CUE_LE_HPC_Med.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.608 0.3041 2.654 0.0734 .
## Residuals 164 18.789 0.1146
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print("R Cue")
## [1] "R Cue"
R_CUE_LE_HPC_Med.aov <- aov(R_CUE_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_CUE_LE_HPC_Med.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.372 0.1862 1.467 0.234
## Residuals 164 20.817 0.1269
print("L Delay")
## [1] "L Delay"
L_DELAY_LE_HPC_Med.aov <- aov(L_DELAY_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_DELAY_LE_HPC_Med.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.003 0.001608 0.067 0.935
## Residuals 164 3.934 0.023988
print("R Delay")
## [1] "R Delay"
R_DELAY_LE_HPC_Med.aov <- aov(R_DELAY_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_DELAY_LE_HPC_Med.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.019 0.009294 0.421 0.657
## Residuals 164 3.624 0.022097
print("L Probe")
## [1] "L Probe"
L_PROBE_LE_HPC_Med.aov <- aov(L_PROBE_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_PROBE_LE_HPC_Med.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.22 0.1103 0.258 0.773
## Residuals 164 70.08 0.4273
print("R Probe")
## [1] "R Probe"
R_PROBE_LE_HPC_Med.aov <- aov(R_PROBE_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_PROBE_LE_HPC_Med.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.80 0.3977 1.094 0.337
## Residuals 164 59.63 0.3636
DFR_HPC_Post_plots[["L_CUE_L3"]] + DFR_HPC_Post_plots[["L_DELAY_L3"]] + DFR_HPC_Post_plots[["L_PROBE_L3"]]+
plot_annotation(title="HPC_Post during DFR task")
DFR_HPC_Post_plots[["R_CUE_L3"]] + DFR_HPC_Post_plots[["R_DELAY_L3"]] + DFR_HPC_Post_plots[["R_PROBE_L3"]]
print("L Cue")
## [1] "L Cue"
L_CUE_L3_HPC_Post.aov <- aov(L_CUE_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_CUE_L3_HPC_Post.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.333 0.1666 1.177 0.311
## Residuals 164 23.222 0.1416
print("R Cue")
## [1] "R Cue"
R_CUE_L3_HPC_Post.aov <- aov(R_CUE_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_CUE_L3_HPC_Post.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.461 0.2303 1.668 0.192
## Residuals 164 22.645 0.1381
print("L Delay")
## [1] "L Delay"
L_DELAY_L3_HPC_Post.aov <- aov(L_DELAY_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_DELAY_L3_HPC_Post.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0423 0.02117 1.582 0.209
## Residuals 164 2.1944 0.01338
print("R Delay")
## [1] "R Delay"
R_DELAY_L3_HPC_Post.aov <- aov(R_DELAY_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_DELAY_L3_HPC_Post.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0567 0.02833 2.439 0.0904 .
## Residuals 164 1.9053 0.01162
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print("L Probe")
## [1] "L Probe"
L_PROBE_L3_HPC_Post.aov <- aov(L_PROBE_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_PROBE_L3_HPC_Post.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.38 0.1876 0.635 0.531
## Residuals 164 48.47 0.2955
print("R Probe")
## [1] "R Probe"
R_PROBE_L3_HPC_Post.aov <- aov(R_PROBE_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_PROBE_L3_HPC_Post.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.22 0.1085 0.361 0.698
## Residuals 164 49.32 0.3007
DFR_HPC_Post_plots[["L_CUE_LE"]] + DFR_HPC_Post_plots[["L_DELAY_LE"]] + DFR_HPC_Post_plots[["L_PROBE_LE"]]+
plot_annotation(title="HPC_Post during DFR task")
DFR_HPC_Post_plots[["R_CUE_LE"]] + DFR_HPC_Post_plots[["R_DELAY_LE"]] + DFR_HPC_Post_plots[["R_PROBE_LE"]]
print("L Cue")
## [1] "L Cue"
L_CUE_LE_HPC_Post.aov <- aov(L_CUE_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_CUE_LE_HPC_Post.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.174 0.08689 0.891 0.412
## Residuals 164 15.999 0.09755
print("R Cue")
## [1] "R Cue"
R_CUE_LE_HPC_Post.aov <- aov(R_CUE_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_CUE_LE_HPC_Post.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.162 0.08123 0.932 0.396
## Residuals 164 14.293 0.08715
print("L Delay")
## [1] "L Delay"
L_DELAY_LE_HPC_Post.aov <- aov(L_DELAY_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_DELAY_LE_HPC_Post.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0065 0.003273 0.18 0.835
## Residuals 164 2.9776 0.018156
print("R Delay")
## [1] "R Delay"
R_DELAY_LE_HPC_Post.aov <- aov(R_DELAY_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_DELAY_LE_HPC_Post.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0014 0.00068 0.05 0.951
## Residuals 164 2.2391 0.01365
print("L Probe")
## [1] "L Probe"
L_PROBE_LE_HPC_Post.aov <- aov(L_PROBE_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_PROBE_LE_HPC_Post.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.02 0.0084 0.025 0.975
## Residuals 164 54.61 0.3330
print("R Probe")
## [1] "R Probe"
R_PROBE_LE_HPC_Post.aov <- aov(R_PROBE_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_PROBE_LE_HPC_Post.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.38 0.1906 0.643 0.527
## Residuals 164 48.63 0.2965
Only see differences in the L probe regions, with med > high.
cortical_thickness_plots[["Cue_RH"]] + cortical_thickness_plots[["Delay_RH"]] + cortical_thickness_plots[["Probe_RH"]] +
plot_annotation(title="Cortical Thickness from DFR Full Mask")
cortical_thickness_plots[["Cue_LH"]] + cortical_thickness_plots[["Delay_LH"]] + cortical_thickness_plots[["Probe_LH"]]
print("L Cue")
## [1] "L Cue"
L_CUE_DFR_thick.aov <- aov(Cue_LH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(L_CUE_DFR_thick.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0131 0.006569 0.739 0.479
## Residuals 164 1.4574 0.008887
print("R Cue")
## [1] "R Cue"
R_CUE_DFR_thick.aov <- aov(Cue_RH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(R_CUE_LE_HPC_Post.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.162 0.08123 0.932 0.396
## Residuals 164 14.293 0.08715
print("L Delay")
## [1] "L Delay"
L_DELAY_DFR_thick.aov <- aov(Delay_LH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(L_DELAY_DFR_thick.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.075 0.03748 1.687 0.188
## Residuals 163 3.622 0.02222
## 1 observation deleted due to missingness
print("R Delay")
## [1] "R Delay"
R_DELAY_DFR_thick.aov <- aov(Delay_RH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(R_DELAY_DFR_thick.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0221 0.01106 0.645 0.526
## Residuals 164 2.8152 0.01717
print("L Probe")
## [1] "L Probe"
L_PROBE_DFR_thick.aov <- aov(Probe_LH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(L_PROBE_DFR_thick.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0506 0.02532 1.38 0.254
## Residuals 164 3.0086 0.01835
print("R Probe")
## [1] "R Probe"
R_PROBE_DFR_thick.aov <- aov(Probe_RH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(R_PROBE_DFR_thick.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.1159 0.05793 3.279 0.0401 *
## Residuals 164 2.8974 0.01767
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(R_PROBE_DFR_thick.aov)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Probe_RH ~ level, data = split_cortical_thickness_DFR[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.06434623 0.004665777 0.12402669 0.0312089
## low-high 0.02676429 -0.032646732 0.08617530 0.5368331
## low-med -0.03758195 -0.097262405 0.02209851 0.2986389
Differences within FPCN (med > high) and VAN (med and low > high).
RS_plots[["FPCN_FPCN"]] + RS_plots[["DMN_DMN"]] + RS_plots[["DAN_DAN"]]+
plot_annotation(title="Resting State Functional Connectivity - Within Networks")
RS_plots[["VAN_VAN"]] + RS_plots[["CO_CO"]] + RS_plots[["visual_visual"]]
print("FPCN")
## [1] "FPCN"
FPCN.aov <- aov(FPCN_FPCN ~ level, data=split_RS[["all"]])
summary(FPCN.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0516 0.025801 3.545 0.0311 *
## Residuals 164 1.1936 0.007278
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(FPCN.aov)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = FPCN_FPCN ~ level, data = split_RS[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.04303510 0.004729856 0.08134034 0.0234141
## low-high 0.02371511 -0.014417200 0.06184741 0.3075572
## low-med -0.01931999 -0.057625235 0.01898525 0.4590698
print("DMN")
## [1] "DMN"
DMN.aov <- aov(DMN_DMN ~ level, data=split_RS[["all"]])
summary(DMN.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0476 0.023814 2.538 0.0821 .
## Residuals 164 1.5387 0.009382
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print("DAN")
## [1] "DAN"
DAN.aov <- aov(DAN_DAN ~ level, data=split_RS[["all"]])
summary(DAN.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0099 0.004967 0.634 0.532
## Residuals 164 1.2843 0.007831
print("VAN")
## [1] "VAN"
VAN.aov <- aov(VAN_VAN ~ level, data=split_RS[["all"]])
summary(VAN.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.1191 0.05955 7.196 0.00101 **
## Residuals 164 1.3572 0.00828
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(VAN.aov)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = VAN_VAN ~ level, data = split_RS[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.04593331 0.005087387 0.08677924 0.0232524
## low-high 0.06309417 0.022432651 0.10375569 0.0009513
## low-med 0.01716086 -0.023685067 0.05800678 0.5818832
print("CO")
## [1] "CO"
CO.aov <- aov(CO_CO ~ level, data=split_RS[["all"]])
summary(CO.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0233 0.011627 1.444 0.239
## Residuals 164 1.3205 0.008052
print("CO")
## [1] "CO"
visual.aov <- aov(visual_visual ~ level, data=split_RS[["all"]])
summary(visual.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0086 0.004293 0.249 0.78
## Residuals 164 2.8272 0.017239
No across RS network differences.
RS_plots[["FPCN_DMN"]] + RS_plots[["FPCN_DAN"]] + RS_plots[["FCPN_VAN"]]+
plot_annotation(title="Resting State Functional Connectivity - Across Networks")
RS_plots[["FPCN_CO"]] + RS_plots[["FPCN_visual"]]
print("FPCN DMN")
## [1] "FPCN DMN"
FPCN_DMN.aov <- aov(FPCN_DMN ~ level, data=split_RS[["all"]])
summary(FPCN_DMN.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0194 0.009688 1.196 0.305
## Residuals 164 1.3289 0.008103
print("FPCN DAN")
## [1] "FPCN DAN"
FPCN_DAN.aov <- aov(FPCN_DAN ~ level, data=split_RS[["all"]])
summary(FPCN_DAN.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0129 0.006428 1.254 0.288
## Residuals 164 0.8405 0.005125
print("FPCN VAN")
## [1] "FPCN VAN"
FPCN_VAN.aov <- aov(FPCN_VAN ~ level, data=split_RS[["all"]])
summary(FPCN_VAN.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0064 0.003187 0.459 0.633
## Residuals 164 1.1396 0.006949
print("FPCN CO")
## [1] "FPCN CO"
FPCN_CO.aov <- aov(FPCN_CO ~ level, data=split_RS[["all"]])
summary(FPCN_CO.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0115 0.005748 0.556 0.574
## Residuals 164 1.6946 0.010333
print("FPCN visual")
## [1] "FPCN visual"
FPCN_visual.aov <- aov(FPCN_visual ~ level, data=split_RS[["all"]])
summary(FPCN_visual.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0004 0.000186 0.025 0.976
## Residuals 164 1.2309 0.007506
Differences in the beta series connectivity for FPCN/HPC connectivity (med > high), FPCN/FFA (med and low > high), and HPC/FFA (med > high)
beta_conn_cue_plots[["FPCN_FPCN_L3"]] + beta_conn_cue_plots[["FPCN_HPC_L3"]] +
plot_annotation(title = "Beta Series Connectivity at High Load")
beta_conn_cue_plots[["FPCN_FFA_L3"]] + beta_conn_cue_plots[["HPC_FFA_L3"]]
FPCN_FPCN_BC_cue_L3.aov <- aov(FPCN_FPCN_L3 ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_FPCN_BC_cue_L3.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.214 0.1070 0.821 0.442
## Residuals 164 21.371 0.1303
FPCN_HPC_BC_cue_L3.aov <- aov(FPCN_HPC_L3 ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_HPC_BC_cue_L3.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.871 0.4355 4.173 0.0171 *
## Residuals 164 17.115 0.1044
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(FPCN_HPC_BC_cue_L3.aov)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = FPCN_HPC_L3 ~ level, data = split_beta_conn_cue[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.17589316 0.03084184 0.32094447 0.0129087
## low-high 0.10547445 -0.03892200 0.24987090 0.1979807
## low-med -0.07041871 -0.21547002 0.07463261 0.4859175
FPCN_FFA_BC_cue_L3.aov <- aov(FPCN_FFA_L3 ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_FFA_BC_cue_L3.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.304 0.6521 6.415 0.00208 **
## Residuals 164 16.672 0.1017
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(FPCN_FFA_BC_cue_L3.aov)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = FPCN_FFA_L3 ~ level, data = split_beta_conn_cue[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.16024635 0.01708777 0.3034049 0.0240437
## low-high 0.20548974 0.06297748 0.3480020 0.0023437
## low-med 0.04524339 -0.09791520 0.1884020 0.7355717
HPC_FFA_BC_cue_L3.aov <- aov(HPC_FFA_L3 ~ level, data = split_beta_conn_cue[["all"]])
summary(HPC_FFA_BC_cue_L3.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.691 0.3457 3.558 0.0307 *
## Residuals 164 15.932 0.0971
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(HPC_FFA_BC_cue_L3.aov)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = HPC_FFA_L3 ~ level, data = split_beta_conn_cue[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.1423694 0.002421533 0.2823173 0.0451697
## low-high 0.1293965 -0.009919540 0.2687126 0.0747075
## low-med -0.0129729 -0.152920794 0.1269750 0.9738527
data_for_plot <- merge(p200_data,p200_beta_conn_cue,by="PTID")
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=FPCN_FFA_L3))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=2,label="r=-0.22**")+
ggtitle("FPCN/FFA L3")
print("FPCN/FFA L3")
## [1] "FPCN/FFA L3"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$FPCN_FFA_L3)
##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$FPCN_FFA_L3
## t = -2.8628, df = 167, p-value = 0.004738
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.35563295 -0.06752927
## sample estimates:
## cor
## -0.2162844
For the load effects, however, we only see FPCN/FPCN (med > high) and FPCN/FFA (low > high).
beta_conn_cue_plots[["FPCN_FPCN_LE"]] + beta_conn_cue_plots[["FPCN_HPC_LE"]] +
plot_annotation(title = "Beta Series Connectivity Load Effect")
beta_conn_cue_plots[["FPCN_FFA_LE"]] + beta_conn_cue_plots[["HPC_FFA_LE"]]
FPCN_FPCN_BC_cue_LE.aov <- aov(FPCN_FPCN_LE ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_FPCN_BC_cue_LE.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.024 0.5122 3.384 0.0363 *
## Residuals 164 24.825 0.1514
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(FPCN_FPCN_BC_cue_LE.aov)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = FPCN_FPCN_LE ~ level, data = split_beta_conn_cue[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.17248805 -0.002204571 0.3471807 0.0537927
## low-high 0.15857460 -0.015329335 0.3324785 0.0818754
## low-med -0.01391345 -0.188606066 0.1607792 0.9806297
FPCN_HPC_BC_cue_LE.aov <- aov(FPCN_HPC_LE ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_HPC_BC_cue_LE.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.315 0.1575 1.411 0.247
## Residuals 164 18.313 0.1117
FPCN_FFA_BC_cue_LE.aov <- aov(FPCN_FFA_LE ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_FFA_BC_cue_LE.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.132 0.5659 3.298 0.0394 *
## Residuals 164 28.141 0.1716
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(FPCN_FFA_BC_cue_LE.aov)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = FPCN_FFA_LE ~ level, data = split_beta_conn_cue[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.15990313 -0.0260913353 0.3458976 0.1074873
## low-high 0.18575564 0.0006008839 0.3709104 0.0490621
## low-med 0.02585251 -0.1601419506 0.2118470 0.9421874
HPC_FFA_BC_cue_LE.aov <- aov(HPC_FFA_LE ~ level, data = split_beta_conn_cue[["all"]])
summary(HPC_FFA_BC_cue_LE.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.10 0.04998 0.314 0.731
## Residuals 164 26.15 0.15943
No differences for the beta series connectivity during delay period.
beta_conn_delay_plots[["FPCN_FPCN_L3"]] + beta_conn_delay_plots[["FPCN_HPC_L3"]] +
plot_annotation(title = "Beta Series Connectivity at High Load")
beta_conn_delay_plots[["FPCN_FFA_L3"]] + beta_conn_delay_plots[["HPC_FFA_L3"]]
FPCN_FPCN_BC_delay_L3.aov <- aov(FPCN_FPCN_L3 ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_FPCN_BC_delay_L3.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.178 0.08922 0.854 0.428
## Residuals 164 17.132 0.10446
FPCN_HPC_BC_delay_L3.aov <- aov(FPCN_HPC_L3 ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_HPC_BC_delay_L3.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.276 0.13786 1.91 0.151
## Residuals 164 11.837 0.07218
FPCN_FFA_BC_delay_L3.aov <- aov(FPCN_FFA_L3 ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_FFA_BC_delay_L3.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.098 0.04920 0.837 0.435
## Residuals 164 9.645 0.05881
HPC_FFA_BC_delay_L3.aov <- aov(HPC_FFA_L3 ~ level, data = split_beta_conn_delay[["all"]])
summary(HPC_FFA_BC_delay_L3.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.29 0.14514 2.294 0.104
## Residuals 164 10.38 0.06327
data_for_plot <- merge(p200_data,p200_beta_conn_delay,by="PTID")
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=FPCN_HPC_L3))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=1.25,label="r=-0.18*")+
ggtitle("FPCN/HPC L3")
print("FPCN/HPC L3")
## [1] "FPCN/HPC L3"
cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$FPCN_HPC_L3)
##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$FPCN_HPC_L3
## t = -2.3698, df = 167, p-value = 0.01894
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.32254842 -0.03023422
## sample estimates:
## cor
## -0.180371
beta_conn_delay_plots[["FPCN_FPCN_LE"]] + beta_conn_delay_plots[["FPCN_HPC_LE"]] +
plot_annotation(title = "Beta Series Connectivity Load Effect")
beta_conn_delay_plots[["FPCN_FFA_LE"]] + beta_conn_delay_plots[["HPC_FFA_LE"]]
FPCN_FPCN_BC_delay_LE.aov <- aov(FPCN_FPCN_LE ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_FPCN_BC_delay_LE.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.018 0.00904 0.192 0.825
## Residuals 164 7.714 0.04704
FPCN_HPC_BC_delay_LE.aov <- aov(FPCN_HPC_LE ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_HPC_BC_delay_LE.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.063 0.03153 0.501 0.607
## Residuals 164 10.319 0.06292
FPCN_FFA_BC_delay_LE.aov <- aov(FPCN_FFA_LE ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_FFA_BC_delay_LE.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.181 0.09059 1.305 0.274
## Residuals 164 11.384 0.06942
HPC_FFA_BC_delay_LE.aov <- aov(HPC_FFA_LE ~ level, data = split_beta_conn_delay[["all"]])
summary(HPC_FFA_BC_delay_LE.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.225 0.1126 1.279 0.281
## Residuals 164 14.433 0.0880
TukeyHSD(HPC_FFA_BC_delay_LE.aov)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = HPC_FFA_LE ~ level, data = split_beta_conn_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.05287245 -0.1860714 0.08032648 0.6165419
## low-high -0.08915921 -0.2217568 0.04343837 0.2526714
## low-med -0.03628675 -0.1694857 0.09691218 0.7958256
No differences in any of the BCT measures.
BCT_plots[["Participation_Coef_Mean"]] + BCT_plots[["Global_Eff"]] + BCT_plots[["Modularity_Louvain_N"]]+
plot_annotation(title="Overall BCT Measures")
print("Mean Participation Coefficient")
## [1] "Mean Participation Coefficient"
partic_coef_mean.aov <- aov(Participation_Coef_Mean ~ level, data = split_BCT[["all"]])
summary(partic_coef_mean.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.04 0.01992 0.277 0.759
## Residuals 164 11.81 0.07199
print("Global Efficiency")
## [1] "Global Efficiency"
global_eff.aov <- aov(Global_Eff ~ level, data = split_BCT[["all"]])
summary(global_eff.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0139 0.006959 0.945 0.391
## Residuals 164 1.2074 0.007362
print("Modularity")
## [1] "Modularity"
modularity.aov <- aov(Modularity_Louvain_N ~ level, data = split_BCT[["all"]])
summary(modularity.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 3.1 1.525 0.211 0.81
## Residuals 164 1186.7 7.236
indiv_partic_coeff_plots[["FrontoParietal"]] + indiv_partic_coeff_plots[["Default"]] + indiv_partic_coeff_plots[["DorsalAttn"]]+
plot_annotation(title="Individual Network Participation Coefficient")
indiv_partic_coeff_plots[["CinguloOperc"]] + indiv_partic_coeff_plots[["VentralAttn"]] + indiv_partic_coeff_plots[["Visual"]]
print("FPCN")
## [1] "FPCN"
FPCN_indiv_coeff.aov <- aov(FrontoParietal ~ level, data = split_indiv_partic_coeff[["all"]])
summary(FPCN_indiv_coeff.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.631 0.3153 1.779 0.172
## Residuals 164 29.067 0.1772
print("DMN")
## [1] "DMN"
DMN_indiv_coeff.aov <- aov(Default ~ level, data = split_indiv_partic_coeff[["all"]])
summary(DMN_indiv_coeff.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.035 0.01756 0.097 0.908
## Residuals 164 29.821 0.18183
print("DAN")
## [1] "DAN"
DAN_indiv_coeff.aov <- aov(DorsalAttn ~ level, data = split_indiv_partic_coeff[["all"]])
summary(DAN_indiv_coeff.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.234 0.11718 1.376 0.256
## Residuals 164 13.969 0.08517
print("CO")
## [1] "CO"
CO_indiv_coeff.aov <- aov(CinguloOperc ~ level, data = split_indiv_partic_coeff[["all"]])
summary(CO_indiv_coeff.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.30 0.1500 1.27 0.284
## Residuals 164 19.37 0.1181
print("VAN")
## [1] "VAN"
VAN_indiv_coeff.aov <- aov(VentralAttn ~ level, data = split_indiv_partic_coeff[["all"]])
summary(VAN_indiv_coeff.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.065 0.03235 0.28 0.756
## Residuals 164 18.981 0.11574
print("visual")
## [1] "visual"
visual_indiv_coeff.aov <- aov(Visual ~ level, data = split_indiv_partic_coeff[["all"]])
summary(visual_indiv_coeff.aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.086 0.04307 0.244 0.783
## Residuals 164 28.893 0.17618